OpenAI: o3 Mini High

OpenAI o3 Mini High is a text model from OpenAI. It is the same underlying model as o3-mini, with the reasoning_effort parameter effectively pinned to high, so you get the most deliberate version of the model's reasoning behaviour rather than a separate architecture. OpenAI positions o3-mini as a cost-efficient language model optimised for STEM reasoning, and this high-effort variant is aimed at work where that extra reasoning depth matters more than latency.

The model accepts text and file inputs and returns text. It has a context window of 200,000 tokens and can produce up to 100,000 output tokens, so long inputs and long structured answers are both workable within a single request. Its knowledge cutoff is 31 October 2023. Reasoning is a first-class part of how the model behaves rather than an add-on, which shapes both its strengths and the way you configure calls to it.

Those capabilities point to fairly specific use cases. Science and mathematics problems, including multi-step derivations and quantitative analysis, are the clearest fit given the model's STEM focus. The combination of file input, a large context window and structured outputs makes it reasonable for tasks such as extracting and reasoning over technical documents, reviewing code or specifications for correctness, and producing schema-constrained JSON that downstream systems can consume. Tool calling extends this to workflows where the model needs to invoke functions or external services as part of a longer chain of reasoning. Because you can request reasoning output, it also suits cases where an auditable trace of the model's thinking is useful alongside the final answer.

Through api14 you call it with the model id "openai/o3-mini-high" using the OpenAI-compatible API, so existing OpenAI SDKs and clients work with a change of base URL and key. The supported request parameters are include_reasoning, max_tokens, reasoning, reasoning_effort, response_format, seed, structured_outputs, tool_choice and tools. In practice that means you can set an output token budget, control whether reasoning content is returned, steer reasoning behaviour, request JSON or schema-conforming responses, fix a seed for reproducibility, and attach tools with a chosen tool_choice policy.

The main thing to weigh is that this is the high-effort configuration of a deliberately cost-efficient model line rather than a frontier model, and reasoning effort is not something you tune down here if you want the variant's intended behaviour. The knowledge cutoff of 31 October 2023 means anything after that date has to come from your prompt, files or tools. If a task does not depend on deep STEM reasoning, a lighter model may be the more sensible default.